Displaying visual analytics of entity data

ABSTRACT

According to an example, in a method for displaying visual analytics of entity data, geographic locations of entities may be plotted as first pixel cells on a first region and as second pixel cells on a second region of a geographic map. A determination may be made that the first pixel cells have a higher degree of overlap with each other in the first region compared to the second pixel cells in the second region. The geographic map may be distorted to enlarge the first region and the first pixel cells may be arranged in the first region in a manner that prevents the first pixel cells from overlapping each other. A color value for each of the pixel cells may be determined from a multi-paired color map that represents two variables corresponding to the entities by color and the pixel cells may be caused to be displayed on the distorted geographic map according to the determined respective color values.

BACKGROUND

Geographic maps are typically employed to display the locations ofvarious objects within a particular area. Along with their respectivegeographic locations, additional information, such as a particularattribute of the various objects, is often provided on the geographicmaps. Conventional geographic maps therefore provide an overview of theobjects and a particular attribute of the objects.

BRIEF DESCRIPTION OF THE DRAWINGS

Features of the present disclosure are illustrated by way of example andnot limited in the following figure(s), in which like numerals indicatelike elements, in which:

FIG. 1 is a simplified diagram of a computing device, which mayimplement various aspects of the methods disclosed herein, according toan example of the present disclosure;

FIGS. 2 and 3, respectively, are flow diagrams of methods for displayingvisual analytics of entity data, according to two examples of thepresent disclosure;

FIGS. 4A-4C, respectively, depict a geographic map at various stages ofimplementation of the method depicted in FIG. 2, according to an exampleof the present disclosure;

FIG. 5 depicts a color map that may be used to determine the colorvalues of the pixel cells depicted in FIGS. 4A-4C, according to anexample;

FIGS. 6A-6C, respectively, depict a geographic map, a two-dimensionalgraph, and a second geographic map, which may be generated at variousstages of implementation of the method depicted in FIG. 3, according toan example;

FIGS. 7A-7E, respectively, depict diagrams pertaining to a particularset of entities in which the methods disclosed herein are implemented,according to an example; and

FIG. 8 is schematic representation of a computing device, which may beemployed to perform various functions of the computing device depictedin FIG. 1, according to an example of the present disclosure.

DETAILED DESCRIPTION

For simplicity and illustrative purposes, the present disclosure isdescribed by referring mainly to an example thereof. In the followingdescription, numerous specific details are set forth in order to providea thorough understanding of the present disclosure. It will be readilyapparent however, that the present disclosure may be practiced withoutlimitation to these specific details. In other instances, some methodsand structures have not been described in detail so as not tounnecessarily obscure the present disclosure. As used herein, the terms“a” and “an” are intended to denote at least one of a particularelement, the term “includes” means includes but not limited to, the term“including” means including but not limited to, and the term “based on”means based at least in part on.

Disclosed herein are methods for displaying visual analytics of entitydata and apparatuses for implementing the methods. In the methods,geographic locations of a plurality of entities may be plotted as firstpixel cells on a first region and as second pixel cells on a secondregion of a geographic map. In addition, a determination may be madethat the first pixel cells have a higher degree of overlap with eachother in the first region compared to the second pixel cells in thesecond region. The geographic map may be distorted to enlarge the firstregion and the first pixel cells may be arranged in the first region ina manner that prevents the first pixel cells from overlapping eachother. Moreover, a color value for each of the first pixel cells and thesecond pixel cells may be determined from a multi-paired color map thatrepresents two variables corresponding to the plurality of entities bycolor and the first pixel cells and the second pixel cells may be causedto be displayed on the distorted geographic map according to thedetermined respective color values.

Through implementation of the methods and apparatuses disclosed herein,visual analytics of high density spatial data may be visually providedas pixel cells on a map in a manner that may enable users tointeractively and easily identify an entity or entities having desiredattributes. In one regard, the visualization of the high density spatialdata may enable pattern recognition and anomaly detection on arelatively large scale. In another regard, the interaction methodsprovided through implementation of the methods and apparatuses disclosedherein generally provide users with the ability to drill down into thedata pertaining to the entities to thus make more informed decisions.

By way of particular example, the entities are hospitals, some of whichmay be located with respect to each other in a relatively dense manner,as may occur in relatively densely populated regions, while others maybe located with respect to each other in a relatively less dense manner,as may occur in more rural regions. In this example, the variablescorresponding to the hospitals may be hospital charges and servicesquality. As discussed in greater detail herein below, the methods andapparatuses disclosed herein may provide an interactive visualization ofthe analytics of the hospital data displayed as pixel cells to enable auser to compare the hospitals based upon their locations as well as datacorresponding to the hospitals. This example is described in detailbelow with respect to FIGS. 7A-7E.

With reference first to FIG. 1, there is shown a simplified blockdiagram 100 of a computing device 102, which may implement variousaspects of the methods disclosed herein, according to an example. Itshould be understood that the computing device 102 depicted in FIG. 1may include additional elements and that some of the elements depictedtherein may be removed and/or modified without departing from a scope ofthe computing device 102.

As shown in FIG. 1, the computing device 102 may include an entity datavisual analytics displaying apparatus 104, a processor 110, aninput/output interface 112, and a data store 114. The entity data visualanalytics displaying apparatus 104 is also depicted as including a dataaccessing module 120, a generating module 122, a pixel cell overlapdetermining module 124, a geographic map distorting module 126, a pixelcell arranging module 128, a color value determining module 130, adisplaying/output module 132, and an input receiving module 134.

The processor 110, which may be a microprocessor, a micro-controller, anapplication specific integrated circuit (ASIC), or the like, is toperform various processing functions in the computing device 102. Theprocessing functions may include invoking or implementing the entitydata visual analytics displaying apparatus 104 and particularly, themodules 120-134 of the entity data visual analytics displaying apparatus104, as discussed in greater detail herein below. According to anexample, the entity data visual analytics displaying apparatus 104 is ahardware device on which is stored various sets of machine readableinstructions. The entity data visual analytics displaying apparatus 104may be, for instance, a volatile or non-volatile memory, such as dynamicrandom access memory (DRAM), electrically erasable programmableread-only memory (EEPROM), magnetoresistive random access memory (MRAM),memristor, flash memory, floppy disk, a compact disc read only memory(CD-ROM), a digital video disc read only memory (DVD-ROM), or otheroptical or magnetic media, and the like, on which software may bestored. In this example, the modules 120-134 may be software modules,e.g., sets of machine readable instructions, stored in entity datavisual analytics displaying apparatus 104.

In another example, the entity data visual analytics displayingapparatus 104 may be a hardware component, such as a chip, and themodules 120-134 may be hardware modules on the hardware component. In afurther example, the modules 120-134 may include a combination ofsoftware and hardware modules.

The processor 110 may store data in the data store 114 and may use thedata in implementing the modules 120-134. For instance, entity data thatis to be used in generating the display of the visual analyticscorresponding to the entity data may be stored in the data store 114. Inaddition, generated versions of maps containing pixel cellscorresponding to the entity data may also be stored in the data store114. In any regard, the data store 114 may be volatile and/ornon-volatile memory, such as DRAM, EEPROM, MRAM, phase change RAM(PCRAM), memristor, flash memory, and the like. In addition, oralternatively, the data store 114 may be a device that may read from andwrite to a removable media, such as, a floppy disk, a CD-ROM, a DVD-ROM,or other optical or magnetic media.

The input/output interface 112 may include hardware and/or software toenable the processor 110 to communicate with other devices. Forinstance, the input/output interface 112 may enable the processor 110 toaccess a network, such as an internal network, the Internet, etc. Theinput/output interface 112 may include a network interface card and mayalso include hardware and/or software to enable the processor 110 tocommunicate with various input and/or output devices 116, such as akeyboard, a mouse, a display, another computing device, etc., throughwhich a user may input instructions into the computing device 102 andmay view outputs from the computing device 102. According to an example,the output device 116 is located outside of a network in which thecomputing device 102 is located. In this example, for instance, thecomputing device 102 may be located in a data center and the outputdevice 116 may be located at an external location such that thecomputing device 102 may communicate data to the output device 116 overan external network, such as the Internet. In one regard, the outputdevice 116, which may be another computing device, may access thecomputing device 102 over a cloud computing environment.

Various manners in which the processor 110 in general, and the modules120-134 in particular, may be implemented are discussed in greaterdetail with respect to the methods 200 and 300 respectively depicted inFIGS. 2 and 3. Particularly, FIGS. 2 and 3, respectively, depict flowdiagrams of methods 200 and 300 for displaying visual analytics ofentity data, according to two examples. It should be apparent to thoseof ordinary skill in the art that the methods 200 and 300 may representgeneralized illustrations and that other operations may be added orexisting operations may be removed, modified, or rearranged withoutdeparting from the scopes of the methods 200 and 300. Generallyspeaking, the processor 110 depicted in FIG. 1 may implement the each ofmethods 200 and 300 through implementation of at least some of themodules 120-134.

The descriptions of the methods 200 and 300 are made with reference tothe diagrams illustrated in FIGS. 4A-4C, 5, and 6A-6C. It should beclearly understood that the diagrams depicted in FIGS. 4A-4C, 5, and6A-6C are for illustrative purposes only and should thus not beconstrued as limiting the scope of the present disclosure in anyrespect.

With reference first to FIG. 2, at block 202, geographic locations of aplurality of entities may be plotted as first pixel cells 410 on a firstregion 402 and as second pixel cells 412 on a second region 404 of ageographic map 400. Particularly, for instance, the processor 110 mayimplement the generating module 122 to plot the geographic locations ofthe entities as the first pixel cells 410 in the first region 402 of thegeographic map 400. The processor 110 may also implement the generatingmodule 122 to plot the geographic locations of the entities as thesecond pixel cells 412 in the second region 404 of the geographic map400. Additionally, third pixel cells 414 may be plotted in the thirdregion 406 and fourth pixel cells 416 may be plotted in the fourthregion in similar manners.

By way of particular example, the geographic map 400 may be a geographicmap of a particular area, such as the United States, a particular Stateor country, a county, a city, a neighborhood, a building, a field, etc.In addition, the regions 402-408 may be respective sections of the area,such as, individual states, individual counties, individual rooms, aregular grid, etc. The entities may be respective objects, buildings,land marks, etc., or collections of data, that are to be displayed onthe geographic map 400. By way of particular example, the entities arehospitals, health care centers, revenue data, energy consumption data,etc.

Although the pixel cells 410-416 are each depicted as circular dots inFIG. 4A, it should be understood that the pixel cells 410-416 may haveany shape, such as squares, triangles, asterisks, textual characters,numbers, etc., without departing from a scope of the present disclosure.In addition, although the pixel cells 410-416 are depicted as having thesame shapes, it should also be understood that some of the pixel cells410-416 may have different shapes with respect to each other withoutdeparting from a scope of the present disclosure. For instance, thedifferent shaped pixel cells may denote that their associated entitieshave different properties or attributes with respect to each other.

As discussed in greater detail below, the pixel cells 410-416 may alsohave any of a plurality of colors, in which the colors of the pixelcells 410-416 denote visual analytics of attributes, e.g., values,corresponding to the entities to which the pixel cells 410-416respectively correspond.

At block 204, a determination may be made that the first pixel cells 410have a higher degree of overlap with each other in the first region 402as compared to the degree of overlap of the second pixel cells 412 inthe second region 404. Particularly, the processor 110 may implement thepixel cell overlap determining module 124 to make this determinationfrom the plotting of the pixel cells in the first and second regions410, 412 of the geographic map 400. That is, the processor 110 maydetermine that a relatively larger number of first pixel cells 410overlap each other than the number of second pixel cells 412 thatoverlap each other, if any. The first pixel cells 410 may have a higherdegree of overlap with respect to other ones of the pixel cells 410, forinstance, because the entities to which the first pixel cells 410correspond may be in relatively close geographic proximities to eachother.

At block 206, the geographic map may be distorted to enlarge the firstregion 402 containing the overlapping first pixel cells 410.Particularly, the processor 110 may implement the geographic mapdistorting module 126 to distort the first region 402 such that thefirst region 402 is enlarged, while keeping the other regions 404-408unchanged. In addition, or alternatively, the processor 110 mayimplement the geographic map distorting module 126 to also distort thesecond region 404, for instance, to reduce the size of the second region404. Thus, for instance, the processor 110 may distort some of theregions 402, 404 by enlarging or reducing those regions.

An example of the map 400 in which the first region 402 has beenenlarged and second to fourth regions 404-408 have been reduced is showin FIG. 4B. As shown in FIG. 4A, the first pixel cells 410 in the firstregion 402 are arranged at a relatively higher density level as comparedwith the second pixel cells 412 in the second region 404. By enlargingthe first region 402, the first pixel cells 410 are able to be arrangedin over a larger space, while still enabling visualization of the entitylocations with respect to each other.

According to an example, the processor 110 may implement a distortiontechnique that attempts to equalize the densities of the pixel cells inthe respective regions 402-408 of the map 400. Thus, for instance, highdensity areas, e.g., the first region 402, may be enlarged, and lowdensity areas, e.g., the second to fourth regions 404-408 may bereduced. The distortion technique may also include receipt of user inputto further distort or to reduce the distortion of the regions 402-408.Thus, for instance, a user may iteratively manipulate the distortion ofthe regions 402-408 through a trial and error process to achieve adesired visualization of the entities on the map 400.

At block 208, the first pixel cells 410 in the first region 402 may bearranged in a manner that prevents the first pixel cells 410 fromoverlapping each other. For instance, the processor 110 may implementthe pixel cell arranging module 128 to arrange the first pixel cells 410in the first region 402 in a manner that prevents the first pixel cells410 from overlapping each other. According to an example, the pixel cellarranging module 128 may include a pixel placement operation. That is,the pixel cell arranging module 128 may cause the processor 110 toiterate through all of the pixel cells in an ordered manner and checkwhether the original position, e.g., pixel position, of a pixel cell isalready occupied by another pixel cell. If the original position of thepixel cell is not occupied by another pixel cell, the pixel cell may beplaced at the original position. However, if the original position isoccupied by another pixel cell, a closest free position, e.g., pixelposition, may be determined and the pixel cell may be placed at theclosest free position.

In instances in which multiple locations at which the pixel cell may beplaced are the same distance away from the original position, the finalposition of the pixel cell may be determined to be a position thatsubstantially optimizes the pixel cell placement based upon color. Thus,for instance, the final position of the pixel cell may be determined tobe the location of the multiple locations that is the furthest away froma similarly colored pixel cell.

At block 210, a color value for each of the first pixel cells 410 andthe second pixel cells 412 may be determined from a multi-paired colormap that represents two variables corresponding to the plurality ofentities by color. For instance, the processor 110 may implement thecolor value determining module 130 to determine the color value for eachof the pixel cells 410-416 from a multi-paired color map. Themulti-paired color map may represent multiple paired, two variables bycolor. For instance, an x-axis of the color map may represent a firstvariable and the y-axis of the color map may represent a secondvariable. In addition, the color map may be created by a bi-linearinterpolation.

According to an example, the color map may be created using the hue,saturation, intensity (HSI) color space. For instance, the HSI colorspace may be used to create a coordinate system in which the origin ofthe coordinate system is mapped to the mean value of both variables(e.g., attributes) of the entities and the four corners of thecoordinate system are mapped to a combination of min/max values of bothvariables (e.g., attributes) of the entities.

An example of a manner in which the final color of a pixel cell in theHSI color space is described with respect to FIG. 5, which depicts acolor map 500, according to an example. Initially, the schema presentedin FIG. 5 may be calculated through implementation of the followingfirst operation:

 INPUT: all values of attribute 1, all values of attribute 2, normalization method  OUTPUT: determine the values of the corners ofthe color map // ======================================================== // first dimension if(normalization == LINEAR){  normMinDimOne= min(attribute1);  normMaxDimOne = max(attribute1); }elseif(normalization == SQRT){  normMinDimOne = Math.sqrt(min(attribute1)); normMaxDimOne = Math.sqrt(max(attribute1)); }else if(normalization ==LOGARITHMIC){  normMinDimOne = Math.log(min(attribute1));  normMaxDimOne= Math.log(max(attribute1)); }  normMeanDimOne = (normMinDimOne +normMaxDimOne) / 2; // ============================================ //second dimension if(normalization == LINEAR){  normMinDimTwo =min(attribute2);  normMaxDimTwo = max(attribute2); }elseif(normalization == SQRT){  normMinDimTwo = Math.sqrt(min(attribute2)); normMaxDimTwo = Math.sqrt(max(attribute2)); }else if(normalization ==LOGARITHMIC){  normMinDimTwo = Math.log(min(attribute2));  normMaxDimTwo= Math.log(max(attribute2)); }  normMeanDimTwo = (normMinDimTwo +normMaxDimTwo) / 2;

As may be noted in the first operation discussed above, the values ofthe corners of the color map 500 may be determined through applicationof one of a linear operation, a square root operation, and a logarithmicoperation.

The color value for a given combination of two attributes (dimOne,dimTwo) may be obtained through implementation of the followingtechniques. Initially, the combination of both attributes may betransferred to the schema presented in FIG. 5 according to a secondoperation, which may calculate the normalized position in the coordinatesystem in the range [−1,+1] for each attribute. The result of the secondoperation may be a vector with a length between 0 and sqrt(2). Thedashed vector 502 depicted in FIG. 5 is an example of the resultingvector. The second operation may include the following:

Input: value of the first attribute (dimOne), value of the secondattribute (dimTwo) Output:the normalized position in the range[−1,+1][−1,+1]. // normalize the input values (linear, sqrt or log)if(normalization == SQRT){  dimOne = Math.sqrt(dimOne);  dimTwo =Math.sqrt(dimTwo); }else if(normalization == LOGARITHMIC){  dimOne =Math.log(dimOne);  dimTwo = Math.log(dimTwo); } // first attributeif(dimOne < normMeanDimOne){  normalizedPosition[0] = (−1) *(1-normalize(dimOne, normMinDimOne, normMeanDimOne); }else{ normalizedPosition[0]  =  normalize(dimOne, normMeanDimOne,normMaxDimOne); } // second attribute if(dimTwo < normMeanDimTwo){ normalizedPosition[1] = (−1) * (1 − (normalize(dimTwo,normMinDimTwo,normMeanDimTwo); }else{ normalizedPosition[1] = normalize(dimTwo, normMeanDimTwo,normMaxDimTwo); } return normalizedPosition;.

The color value for the pixel cell may then be calculated according tothe HSI colors pace with the following values:

Hue=angle/360*6.0, wherein the “angle” is the angle in degrees betweenthe vector and a starting vector 504.

Saturation=length*(1+1.0/3.0).

Intensity=1.2.

With reference back to FIG. 2, at block 212, the first pixel cells 410and the second pixel cells 412 may be caused to be displayed on thedistorted geographic map according to the determined respective colorvalues. For instance, the processor 110 may implement thedisplaying/outputting module 132 to cause the first pixel cells 410 andthe second pixel cells 412 to be displayed on the distorted geographicmap 400 according to the determined respective color values. An exampleof the distorted geographic map 400 containing the pixel cells 410-416is depicted in FIG. 4C. As shown in that figure, different ones of thepixel cells 410-416 may have different colors with respect to eachother, in which the different colors denote that entities correspondingto the pixel cells 410-416 have different combinations of attributeswith respect to each other.

According to an example, at block 212, the distorted geographic mapdisplaying the first pixel cells 410 and the second pixel cells 412 maybe caused to be displayed on a display through the input/outputinterface 112. In another example, at block 212, the distortedgeographic map displaying the first pixel cells 410 and the second pixelcells 412 may be caused to be outputted to another device 116, e.g.,another computing device, through the input/output interface 112. By wayof particular example, the distorted geographic map may be outputtedover the Internet from the computing device 102 to the output device116.

Turning now to FIG. 3, at block 302, data pertaining to a plurality ofentities may be accessed, in which the data includes the geographiclocations of the plurality of entities and variables corresponding tothe plurality of entities. For instance, the processor 110 may implementthe data accessing module 120 to access the data. In addition, theprocessor 110 may implement the data accessing module 120 to access thedata from any of a number of sources. For instance, the processor 110may access the data pertaining to the entities from publicly and/orprivately available data sources. In another example, the data may havebeen stored in the data store 114 and the processor 110 may access thedata from the data store 114.

At block 304, blocks 202-212 of the method 200 may be implemented tocause the first pixel cells 410 and the second pixel cells 412 to bedisplayed on the distorted geographic map 400 according to thedetermined respective color values as discussed above with respect toblock 212. Thus, following block 304, the distorted map 400 as depictedin FIG. 4C may be displayed to a user.

At block 306, a first input may be received, in which the first inputselects a subset of at least one of the first pixel cells 410 and thesecond pixel cells 412. For instance, the processor 110 may implementthe input receiving module 134 to receive the first input from the user.According to an example, the first input may be a rubber-banded sectionof the geographic map 400. For instance, as shown in FIG. 6A, the firstinput 602 may be a rubber-banded section that include a plurality ofpixel cells 604-610. The rubber-banded section may denote an area thatis highlighted by a user.

At block 308, in response to receipt of the first input 602, atwo-dimensional graph that depicts an arrangement of the selected subsetof the at least one of the first pixel cells and the second pixel cellsaccording to the two variables corresponding to the plurality ofentities may be generated. For instance, the processor 110 may implementthe generating module 122 to generate the two-dimensional graph. Anexample of the two-dimensional graph 620 is depicted in FIG. 6B. Asshown in FIG. 6B, the two-dimensional graph 620 includes a y-axis thatrepresents a first variable (variable one) and an x-axis that representsa second variable (variable two). The graph 620 is also depicted asincluding four corners, in which each of the corners represents a pairof values of the first variable and the second variable. A first corner622 represents a minimum value of the first variable and a minimum valueof the second variable. A second corner 624 represents a minimum valueof the first variable and a maximum value of the second variable. Athird corner 626 represents a maximum value of the first variable and aminimum value of the second variable. A fourth corner 628 represents amaximum value of the first variable and the maximum value of the secondvariable.

As also shown in FIG. 6B, each of the pixel cells 604-610 in the subsetidentified in the first input 602 in FIG. 6A is depicted as beingplotted within the graph 620. That is, the pixel cells 604-610 aredepicted as being plotted in the graph 620 according to the first andsecond variables of the entities to which the pixel cells 604-610respectively correspond. Thus, for instance, a pixel cell 604 maycorrespond to an entity having a lower first variable and a lower secondvariable than the entity to which a pixel cell 606 corresponds.

At block 310, the two-dimensional graph 620 including the pixel cells604-610 may be displayed. For instance, the processor 110 may implementthe displaying/outputting module 132 to display the two-dimensionalgraph 620 on the input/output device 116. The graphical depiction of thepixel cells 604-610 in the graph 620 may therefore enable a user torelatively easily see the differences between the entities representedby the pixel cells 604-610.

At block 312, a second input may be received, in which the second inputselects a further subset of the first pixel cells depicted in thetwo-dimensional graph. For instance, the processor 110 may implement theinput receiving module 134 to receive the second input from a user.According to an example, the second input may be a rubber-banded sectionof the two-dimensional graph. For instance, as shown in FIG. 6B, thesecond input 630 may be a rubber-banded section that includes a furthersubset of the pixel cells 604-610 selected in the first input 602.

At block 314, in response to receipt of the second input 630, a secondgeographic map that depicts the further subset of the first pixel cellsmay be generated according to the respective geographic locations of theplurality of entities corresponding to the further subset of the firstpixel cells. For instance, the processor 110 may implement thegenerating module 122 to generate the second geographic map. An exampleof the second geographic map 650 is depicted in FIG. 6C.

As shown in FIG. 6C, the second geographic map 650 may be an enlargedsection of the geographic map 400 depicted in FIG. 6A. Particularly, thesecond geographic map 650 may be an enlarged portion of the sectionselected as the first input 602. In other examples, however, the secondgeographic map 650 may depict other portions of the geographic map 400.

As also shown in FIG. 6C, the pixel cells 604, 608, and 610 that wereincluded in the second input 630 (FIG. 6B) may be highlighted such thatthese pixel cells are differentiated from the pixel cell 606, which wasnot included in the second input 630. In the second geographic map 650,the pixel cells 604, 608 and 610 may have a different color or may havesome other feature to distinguish those pixel cells from the pixel cell606. In one regard, therefore, the second geographic map 650 maygraphically show the locations of the entities identified by the user ashaving the values of the desired pairs of first and second variables. Inanother example, only those pixel cells 604, 608, and 610 that wereincluded in the second input 630 may be depicted in the secondgeographic map 650.

At block 316, the second geographic map 650 including the highlightedpixel cells 604, 608, and 610 may be displayed. For instance, theprocessor 110 may implement the displaying/outputting module 132 todisplay the second geographic map 650 on the input/output device 116. Auser may select one the entities corresponding to the pixel cells 604,608, and 610 as a final desired entity. In one regard, therefore, themethod 300 may enable a user to interact with the display of the pixelcells 410, 412 and the geographic map 400.

According to a particular example, the entities are hospitals, a firstvariable is hospital charges, and a second variable is services quality(e.g., re-admit ratio). In this example, data pertaining to thehospitals may be accessed from publically available information, such asinformation available from the Center for Medicare and MedicaidServices. In addition, the geographic locations of the hospitals may beplotted on a geographic map as first pixel cells and second pixel cells(block 202). An example of a geographic map on which pixel cells havebeen plotted on multiple regions is depicted in the diagram 700 in FIG.7A. As shown in that diagram 700, the geographic map is of the UnitedStates, and each of the regions is a particular state. In addition, itmay also be seen that the hospitals in some of the more populated areas,such as Los Angeles and New York City, are in a relatively more denseformation as compared with other areas and thus, there is a relativelyhigh level of overlap among those pixel cells. Moreover, although notexplicitly depicted due to the limitations of the black-and-white image,each of the pixel cells representing a hospital may be displayed with acolor that denotes, for instance, an average hospital charge.

Turning now to FIG. 7B, there is shown a diagram 710, which depicts adistorted version of the geographic map depicted in the diagram 700, andwhich may have been generated and displayed through implementation ofblocks 202-212 (FIG. 2). As may be seen in the diagram 710, the regionscontaining Los Angeles and New York City have been enlarged to thusenable the pixel cells representing the hospitals in those locations toeither not overlap each other or to substantially minimize the amount ofoverlap among those pixel cells. As also shown, other regions containinga relatively sparser arrangement of pixel cells are reduced in sized.Moreover, although not explicitly depicted due to the limitations of theblack-and-white image, each of the pixel cells representing a hospitalmay be displayed with a color according to the two-dimensional color mapshown in the diagram 710. The colors in the color map denote twovariables, hospital charges and excess readmission ratios, and may begenerated as discussed above.

With reference now to FIG. 7C, there is shown a diagram 730, which issimilar to the diagram 7B, but depicts a first input that selects asubset of a first set of pixel cells as denoted by the dashed box (FIG.3, block 306). In this example, the first input is of an area of theEast Coast. Responsive to receipt of the first input, a two-dimensionalgraph may be generated and displayed, as shown in the diagram 740 inFIG. 7D, and as discussed in greater detail with respect to blocks 308and 310 (FIG. 3). As shown, the two-dimensional graph depicts pixelcells of the hospitals in the area selected from the geographic map(FIG. 70) according to the amounts that the respective hospitals chargeand their excess readmission ratios. Thus, the hospitals in the bottomleft section of the graph may be identified as having the lowest chargesand the lowest excess readmission ratios.

A second input that selects a subset of the pixel cells depicted in thetwo-dimensional graph may be received (FIG. 2, block 312). The secondinput selection is denoted by the dashed box in FIG. 7D. In thisexample, the second input is of the hospitals located in the bottom leftsection of the graph. Responsive to receipt of the second input, asecond geographic map that depicts the subset of the pixel cellsdepicted in the graph is generated and displayed, as shown in thediagram 750 in FIG. 7E, and as also discussed in greater detail withrespect to blocks 314 and 316 (FIG. 3). As shown, the second geographicmap is an enlarged portion of the geographic map depicted in the diagram710 in which the selected subset of pixel cells are depicted. That is,for instance, the second geographic map may depict only those pixelcells that are included in the dashed box of the second input. In otherexamples, additional pixel cells may be depicted in the secondgeographic map, but the selected pixel cells may be distinguished fromthose additional pixel cells through, for instance, different colors,flashing, embellishments, etc. In any regard, the pixel cells depictedin the diagram 750 may be displayed to have colors that correspond tothe colors indicated in the two-dimensional graph depicted in FIG. 7D.

In the examples depicted in FIGS. 7A-7E, the user may be a healthinsurance payee or an insurance company who is interested in evaluatinggeographic variations in payments and quality of services. For example,a hospital that wants to improve outreach of cancer screening servicesto the community may implement the methods 200 or 300 to quicklyidentify local neighborhoods that have reduced access to care.Similarly, an insurance company may implement the methods 200 or 300 toidentify high users of health care services, and develop preventivemedicine policies that reduce cost and improve the quality of care.Visualizing spatial patterns of services as provided throughimplementation of the methods 200 or 300 may allow the end user todetermine the effect of specific policies or to detect anomalouspatterns such as disease outbreaks.

Although a particular example directed to hospitals has been described,it should be understood that the methods and apparatuses disclosedherein may be applied to other applications, such as revenue, energy,oil/gas industries, etc. Other examples in which the methods andapparatuses disclosed herein may be applied include, for instance, largescale agricultural businesses to use geospatial data to track plantdisease and predict the spread of the plant disease based on weatherpatterns, nationwide call centers to discover patterns in customersatisfaction that lead to an early detection of product issues, etc.

Some or all of the operations set forth in the methods 200 and 300 maybe contained as utilities, programs, or subprograms, in any desiredcomputer accessible medium. In addition, the methods 200 and 300 may beembodied by computer programs, which may exist in a variety of formsboth active and inactive. For example, they may exist as machinereadable instructions, including source code, object code, executablecode or other formats. Any of the above may be embodied on anon-transitory computer readable storage medium.

Examples of non-transitory computer readable storage media includeconventional computer system RAM, ROM, EPROM, EEPROM, and magnetic oroptical disks or tapes. It is therefore to be understood that anyelectronic device capable of executing the above-described functions mayperform those functions enumerated above.

Turning now to FIG. 8, there is shown a schematic representation of acomputing device 800, which may be employed to perform various functionsof the computing device 102 depicted in FIG. 1, according to an example.The computing device 800 may include a processor 802, a display 804,such as a monitor; a network interface 808, such as a Local Area NetworkLAN, a wireless 802.11x LAN, a 3G mobile WAN or a WiMax WAN; and acomputer-readable medium 810. Each of these components may beoperatively coupled to a bus 812. For example, the bus 812 may be anEISA, a PCI, a USB, a FireWire, a NuBus, or a PDS.

The computer readable medium 810 may be any suitable medium thatparticipates in providing instructions to the processor 802 forexecution. For example, the computer readable medium 810 may benon-volatile media, such as an optical or a magnetic disk; volatilemedia, such as memory. The computer-readable medium 810 may also storean entity data visual analytics displaying machine readable instructions814, which may perform the methods 200 and/or 300 and may include themodules 120-134 of the entity data visual analytics displaying apparatus104 depicted in FIG. 1. In this regard, the entity data visual analyticsdisplaying machine readable instructions 814 may include a dataaccessing module 120, a generating module 122, a pixel cell overlapdetermining module 124, a geographic map distorting module 126, a pixelcell arranging module 128, a color value determining module 130, adisplaying/output module 132, and an input receiving module 134.

Although described specifically throughout the entirety of the instantdisclosure, representative examples of the present disclosure haveutility over a wide range of applications, and the above discussion isnot intended and should not be construed to be limiting, but is offeredas an illustrative discussion of aspects of the disclosure.

What has been described and illustrated herein is an example of thedisclosure along with some of its variations. The terms, descriptionsand figures used herein are set forth by way of illustration only andare not meant as limitations. Many variations are possible within thespirit and scope of the disclosure, which is intended to be defined bythe following claims—and their equivalents—in which all terms are meantin their broadest reasonable sense unless otherwise indicated.

What is claimed is:
 1. A method for displaying visual analytics ofentity data, said method comprising: plotting geographic locations of aplurality of entities as first pixel cells on a first region and assecond pixel cells on a second region of a geographic map; determiningthat the first pixel cells have a higher degree of overlap with eachother in the first region compared to the second pixel cells in thesecond region; distorting the geographic map to enlarge the firstregion; arranging the first pixel cells in the first region in a mannerthat prevents the first pixel cells from overlapping each other;determining a color value for each of the first pixel cells and thesecond pixel cells from a multi-paired color map that represents twovariables corresponding to the plurality of entities by color; andcause, by a processor, the first pixel cells and the second pixel cellsto be displayed on the distorted geographic map according to thedetermined respective color values.
 2. The method according to claim 1,further comprising: accessing data pertaining to the plurality ofentities, wherein the data includes the geographic locations of theplurality of entities and the two variables corresponding to theplurality of entities.
 3. The method according to claim 1, furthercomprising: receiving a first input that selects a subset of at leastone of the first pixel cells and the second pixel cells; and in responseto receipt of the first input, generating a two-dimensional graph thatdepicts an arrangement of the selected subset of the at least one of thefirst pixel cells and the second pixel cells according to the twovariables corresponding to the plurality of entities.
 4. The methodaccording to claim 3, further comprising: receiving a second input thatselects a further subset of the first pixel cells depicted in thetwo-dimensional graph; and in response to receipt of the second input,generating a second geographic map that depicts the further subset ofthe first pixel cells according to the respective geographic locationsof the plurality of entities corresponding to the further subset of thefirst pixel cells.
 5. The method according to claim 4, whereingenerating the second geographic map further comprises generating thesecond geographic map to be an enlarged portion of the geographic map inwhich the selected further subset of the first pixel cells are depicted.6. The method according to claim 4, wherein the first input comprises arubber-banded section of the geographic map and the second inputcomprises a rubber-banded section of the two-dimensional graph.
 7. Themethod according to claim 1, wherein distorting the geographic mapfurther comprises: determining a density of the first pixel cells in thefirst region; determining a density of the second pixel cells in thesecond region; and distorting the geographic map to distort the firstregion and the second region based upon the relative densities of thefirst pixel cells in the first region and the second pixel cells in thesecond region.
 8. The method according to claim 1, wherein arranging thefirst pixel cells in the first region in a manner that prevents thefirst pixel cells from overlapping each other further comprises:positioning an initial pixel cell at a first location in the firstregion; determining that a next pixel cell is to overlap the initialpixel cell; iterating through data points around the first location inan ordered manner to identify an empty location; and positioning thenext pixel cell at the empty location.
 9. The method according to claim1, wherein the plurality of entities comprise a plurality of hospitals.10. An apparatus for displaying visual analytics of entity data, saidapparatus comprising: a processor; a memory on which is stored machinereadable instructions to cause the processor to: access data pertainingto a plurality of entities, wherein the data includes the geographiclocations of the plurality of entities and two variables correspondingto the plurality of entities; plot geographic locations of the pluralityof entities as first pixel cells on a first region and as second pixelcells on a second region of a geographic map; determine that the firstpixel cells have a higher degree of overlap with each other in the firstregion compared to the second pixel cells in the second region; distortthe geographic map to enlarge the first region; arrange the first pixelcells in the first region in a manner that prevents the first pixelcells from overlapping each other; determine a color value for each ofthe first pixel cells and the second pixel cells from a multi-pairedcolor map that represents the two variables corresponding to theplurality of entities by color; and cause the first pixel cells and thesecond pixel cells to be displayed on the distorted map, wherein thefirst pixel cells and the second pixel cells have colors correspondingto the determined respective color values.
 11. The apparatus accordingto claim 10, wherein the machine readable instructions are further tocause the processor to: receive a first input that selects a subset ofat least one of the first pixel cells and the second pixel cells; and inresponse to receipt of the first input, generate a two-dimensional graphthat depicts an arrangement of the selected subset of the at least oneof the first pixel cells and the second pixel cells according to the twovariables corresponding to the plurality of entities.
 12. The apparatusaccording to claim 10, wherein the machine readable instructions arefurther to cause the processor to: receive a second input that selects afurther subset of the first pixel cells depicted in the two-dimensionalgraph; and in response to receipt of the second input, generate a secondgeographic map that depicts the further subset of the first pixel cellsaccording to the respective geographic locations of the plurality ofentities corresponding to the further subset of the first pixel cells.13. The apparatus according to claim 11, wherein, to distort thegeographic map, the machine readable instructions are further to causethe processor to determine a density of the first pixel cells in thefirst region; determine a density of the second pixel cells in thesecond region; and distort the geographic map to distort the firstregion and the second region based upon the relative densities of thefirst pixel cells in the first region and the second pixel cells in thesecond region.
 14. The apparatus according to claim 11, wherein, toarrange the first pixel cells in the first region in a manner thatprevents the first pixel cells from overlapping each other, the machinereadable instructions are further to cause the processor to: position aninitial pixel cell at a first location in the first region; determinethat a next pixel cell is to overlap the initial pixel cell; iteratethrough data points around the first location in an ordered manner toidentify an empty location; and position the next pixel cell at theempty location.
 15. A non-transitory computer readable storage medium onwhich is stored machine readable instructions that when executed by aprocessor cause the processor to: plot geographic locations of aplurality of entities as first pixel cells on a first region and assecond pixel cells on a second region of a geographic map; determinethat the first pixel cells have a higher degree of overlap with eachother in the first region compared to the second pixel cells in thesecond region; distort the geographic map to enlarge the first region;arrange the first pixel cells in the first region in a manner thatprevents the first pixel cells from overlapping each other; determine acolor value for each of the first pixel cells and the second pixel cellsfrom a multi-paired color map that represents two variablescorresponding to the plurality of entities by color; and cause the firstpixel cells and the second pixel cells to be displayed on the distortedmap, wherein the first pixel cells and the second pixel cells havecolors corresponding to the determined respective color values.